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Leading insurance companies are reinventing their products and customer engagement strategies to meet changing customer needs in real time. For this to work, they need both customer data from connected devices and IoT devices, as well as advanced data analytics.
The insurance industry has always been data-driven. Risk models and actuarial analytics have been and will continue to be important to how the industry allocates capital and evaluates/prices risk.
The need to evolve data analytics has more to do with adapting to new customer behaviors and expectations. The ever-growing volume of customer data coming from the Internet of Everything is prompting insurers to collect and use it in new ways.
Customers are looking for new and better solutions
In every industry, we see companies delivering actionable, real-time offerings with advanced data analytics to conquer the market. Customers are willing to share their data when it is used to provide value to them.
Insurers that improve their analytical capabilities are better equipped to offer this kind of relevancy to clients. They can provide ongoing support to clients at every stage of interaction – from the conclusion of a contract to the maintenance of insurance policies and claims.
3 layers of insurance industry data analytics
1. Descriptive analytics usually combined with automation solutions for risk insurance and claims processing. Such analytics are based on certain data attributes from the past and present, historical risk patterns and current market conditions.
2. Predictive analytics allow insurers to look into the future and, using behavioral models, better understand how a client is likely to respond to potential risks. The more customer data that enters the model, the more complete the individual risk profile becomes and the more accurate the predictions become.
3. Prescriptive analytics This is how insurers are beginning to create strategies to help clients mitigate and manage risk. This requires large-scale, real-time optimization of customer data and the insurer’s product portfolio in order to provide contextual, real-time recommendations in the moment.
Building trust through the responsible use of customer data
From the pandemic to climate change, customers face heightened insecurities about their safety and well-being. They also wonder if their data will be used responsibly, but they are willing to share it in exchange for value.
Using customer data to create relevant, real-time usage and behavior-based offers that help customers mitigate, manage and recover losses can help insurers build customer confidence. This is the value that advanced data analytics can bring to both the client of the insurance company and the insurance company itself.
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Disclaimer: This content is provided for general informational purposes and is not intended to be used as a substitute for consultation with our professional advisors.
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